Abstract
The work we present here is based on the recent extension of the syntax of the Biological Expression Language (BEL), which now allows for the representation of genetic variation information in cause-and-effect models. In our article, we describe, how genetic variation information can be used to identify candidate disease mechanisms in diseases with complex aetiology such as Alzheimer’s disease and Parkinson’s disease. In those diseases, we have to assume that many genetic variants contribute moderately to the overall dysregulation that in the case of neurodegenerative diseases has such a long incubation time until the first clinical symptoms are detectable. Owing to the multilevel nature of dysregulation events, systems biomedicine modelling approaches need to combine mechanistic information from various levels, including gene expression, microRNA (miRNA) expression, protein–protein interaction, genetic variation and pathway. OpenBEL, the open source version of BEL, has recently been extended to match this requirement, and we demonstrate in our article, how candidate mechanisms for early dysregulation events in Alzheimer’s disease can be identified based on an integrative mining approach that identifies ‘chains of causation’ that include single nucleotide polymorphism information in BEL models.
Highlights
Barabasi et al [1] assert ‘given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network’
The work we present here is based on the recent extension of the syntax of the Biological Expression Language (BEL), which allows for the representation of genetic variation information in cause-and-effect models
OpenBEL, the open source version of BEL, has recently been extended to match this requirement, and we demonstrate in our article, how candidate mechanisms for early dysregulation events in Alzheimer’s disease can be identified based on an integrative mining approach that identifies ‘chains of causation’ that include single nucleotide polymorphism information in BEL models
Summary
BEL has the potential to impact scientific literature, by introducing computable expressions in scientific publishing, which could be integrated efficiently into existing knowledge environment [113] These causal-reasoning models can provide a valuable addition to the biologists to interpret the gene expression data [114]. A SNP rs3754048, with allele G, in the promoter of APH1A gene, might alter the binding ability of YY1 transcription factor, resulting in an increased level of APH1A and c-secretase activity to facilitate Ab42 generation [121] All these players in the non-amyloidogenic pathway are trans-membrane proteins, which traffic through the endocytic pathway [122], where these proteins are internalized from the plasma membrane and recycled back to the surface (as in early endosomes and recycling endosomes), or, alternatively, sorted to degradation We searched diseased BEL models to identify the functional impacts of these genes on AD, PD or neurodegenerative diseases (see Table 4)
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